text: import collections
  import inspect
  import os
  import warnings
  from abc import ABC, abstractmethod
  from argparse import Namespace
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  
  import torch
  import torch.distributed as torch_distrib
  from torch import Tensor
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  
  from pytorch_lightning import _logger as log
  from pytorch_lightning.core.grads import GradInformation
  from pytorch_lightning.core.hooks import ModelHooks
  from pytorch_lightning.core.memory import ModelSummary
  from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
  from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  
  try:
      import torch_xla.core.xla_model as xm
  except ImportError:
      XLA_AVAILABLE = False
  else:
      XLA_AVAILABLE = True
  
  
  class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          #: Current dtype
          self.dtype = torch.FloatTensor
  
          self.exp_save_path = None
  
          #: The current epoch
          self.current_epoch = 0
  
          #: Total training batches seen across all epochs
          self.global_step = 0
  
          self.loaded_optimizer_states_dict = {}
  
          #: Pointer to the trainer object
          self.trainer = None
  
          #: Pointer to the logger object
          self.logger = None
          self.example_input_array = None
  
          #: True if your model is currently running on GPUs.
          #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.
          self.on_gpu = False
  
          #: True if using dp
          self.use_dp = False
  
          #: True if using ddp
          self.use_ddp = False
  
          #: True if using ddp2
          self.use_ddp2 = False
  
          #: True if using amp
          self.use_amp = False
  
          self.hparams = None
  
      def print(self, *args, **kwargs) -> None:
          r"""
          Prints only from process 0. Use this in any distributed mode to log only once
  
          Args:
              x (object): The thing to print
  
          Examples:
              .. code-block:: python
  
                  # example if we were using this model as a feature extractor
                  def forward(self, x):
                      self.print(x, 'in loader')
  
          """
          if self.trainer.proc_rank == 0:
              print(*args, **kwargs)
  
      @abstractmethod
      def forward(self, *args, **kwargs):
          r"""
          Same as torch.nn.Module.forward(), however in Lightning you want this to define
          the  operations you want to use for prediction (ie: on a server or as a feature extractor).
  
          Normally you'd call self() from your training_step() method.
          This makes it easy to write a complex system for training with the outputs
          you'd want in a prediction setting.
  
          Args:
              x (tensor): Whatever  you decide to define in the forward method
  
          Return:
              Predicted output
  
          Examples:
              .. code-block:: python
  
                  # example if we were using this model as a feature extractor
                  def forward(self, x):
                      feature_maps = self.convnet(x)
                      return feature_maps
  
                  def training_step(self, batch, batch_idx):
                      x, y = batch
                      feature_maps = self(x)
                      logits = self.classifier(feature_maps)
  
                      # ...
                      return loss
  
                  # splitting it this way allows model to be used a feature extractor
                  model = MyModelAbove()
  
                  inputs = server.get_request()
                  results = model(inputs)
                  server.write_results(results)
  
                  # -------------
                  # This is in stark contrast to torch.nn.Module where normally you would have this:
                  def forward(self, batch):
                      x, y = batch
                      feature_maps = self.convnet(x)
                      logits = self.classifier(feature_maps)
                      return logits
  
          """
  
      def training_step(self, *args, **kwargs) -> Union[
          int, Dict[str, Union[Tensor, Dict[str, Tensor]]]
      ]:
          r"""return loss, dict with metrics for tqdm
  
          Args:
              batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                  dataloader. A tensor, tuple or list
              batch_idx (int): Integer displaying index of this batch
              optimizer_idx (int): If using multiple optimizers, this argument will also be present.
              hiddens(:`Tensor <https://pytorch.org/docs/stable/tensors.html>`_):
                  Passed in if truncated_bptt_steps > 0.
  
          Return:
              dict with loss key and optional log, progress keys
               if implementing training_step, return whatever you need in that step:
  
                  - loss -> tensor scalar [REQUIRED]
                  - progress_bar -> Dict for progress bar display. Must have only tensors
                  - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          In this step you'd normally do the forward pass and calculate the loss for a batch.
          You can also do fancier things like multiple forward passes or something model specific.
  
          Examples:
              .. code-block:: python
  
                  def training_step(self, batch, batch_idx):
                      x, y, z = batch
  
                      # implement your own
                      out = self(x)
                      loss = self.loss(out, x)
  
                      logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)
  
                      # if using TestTubeLogger or TensorBoardLogger you can nest scalars
                      logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)
  
                      output = {
                          'loss': loss, # required
                          'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)
                          'log': logger_logs
                      }
  
                      # return a dict
                      return output
  
              If you define multiple optimizers, this step will be called with an additional
              `optimizer_idx` param.
  
              .. code-block:: python
  
                  # Multiple optimizers (ie: GANs)
                  def training_step(self, batch, batch_idx, optimizer_idx):
                      if optimizer_idx == 0:
                          # do training_step with encoder
                      if optimizer_idx == 1:
                          # do training_step with decoder
  
  
              If you add truncated back propagation through time you will also get an additional
               argument with the hidden states of the previous step.
  
              .. code-block:: python
  
                  # Truncated back-propagation through time
                  def training_step(self, batch, batch_idx, hiddens):
                      # hiddens are the hiddens from the previous truncated backprop step
                      ...
                      out, hiddens = self.lstm(data, hiddens)
                      ...
  
                      return {
                          "loss": ...,
                          "hiddens": hiddens  # remember to detach() this
                      }
  
              You can also return a -1 instead of a dict to stop the current loop. This is useful
               if you want to break out of the current training epoch early.
  
          Notes:
              The presented loss value in progress bar is smooth (average) over last values,
               so it differs from values set in train/validation step.
          """
  
      def training_end(self, *args, **kwargs):
          """
          Warnings:
              Deprecated in v0.7.0. use training_step_end instead
          """
  
      def training_step_end(self, *args, **kwargs) -> Dict[
          str, Union[Tensor, Dict[str, Tensor]]
      ]:
          """
          Use this when training with dp or ddp2 because training_step will operate
          on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          .. note:: If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
              training_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in `training_step` for each batch part.
  
          Return:
              dictionary with loss key and optional log, progress keys:
                  - loss -> tensor scalar [REQUIRED]
                  - progress_bar -> Dict for progress bar display. Must have only tensors
                  - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          In this case you should define training_step_end to perform those calculations.
  
          Examples:
              .. code-block:: python
  
                  # WITHOUT training_step_end
                  # if used in DP or DDP2, this batch is 1/num_gpus large
                  def training_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      loss = self.softmax(out)
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
                  # --------------
                  # with training_step_end to do softmax over the full batch
                  def training_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      return {'out': out}
  
                  def training_step_end(self, outputs):
                      # this out is now the full size of the batch
                      out = outputs['out']
  
                      # this softmax now uses the full batch size
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
          .. seealso::
              see the :ref:`multi-gpu-training` guide for more details.
          """
  
      def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:
          r"""
          Operate on a single batch of data from the validation set
          In this step you'd might generate examples or calculate anything of interest like accuracy.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              val_outs = []
              for val_batch in val_data:
                  out = validation_step(train_batch)
                  val_outs.append(out)
                  validation_epoch_end(val_outs)
  
          Args:
              batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                  dataloader. A tensor, tuple or list
              batch_idx (int): The index of this batch
              dataloader_idx (int): The index of the dataloader that produced this batch
                  (only if multiple val datasets used)
  
          Return:
              Dict or OrderedDict - passed to validation_epoch_end.
              If you defined validation_step_end it will go to that first.
  
          .. code-block:: python
  
              # pseudocode of order
              out = validation_step()
              if defined('validation_step_end'):
                  out = validation_step_end(out)
              out = validation_epoch_end(out)
  
  
          .. code-block:: python
  
              # if you have one val dataloader:
              def validation_step(self, batch, batch_idx)
  
              # if you have multiple val dataloaders:
              def validation_step(self, batch, batch_idx, dataloader_idx)
  
          Examples:
              .. code-block:: python
  
                  # CASE 1: A single validation dataset
                  def validation_step(self, batch, batch_idx):
                      x, y = batch
  
                      # implement your own
                      out = self(x)
                      loss = self.loss(out, y)
  
                      # log 6 example images
                      # or generated text... or whatever
                      sample_imgs = x[:6]
                      grid = torchvision.utils.make_grid(sample_imgs)
                      self.logger.experiment.add_image('example_images', grid, 0)
  
                      # calculate acc
                      labels_hat = torch.argmax(out, dim=1)
                      val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
  
                      # all optional...
                      # return whatever you need for the collation function validation_end
                      output = OrderedDict({
                          'val_loss': loss_val,
                          'val_acc': torch.tensor(val_acc), # everything must be a tensor
                      })
  
                      # return an optional dict
                      return output
  
              If you pass in multiple val datasets, validation_step will have an additional argument.
  
              .. code-block:: python
  
                  # CASE 2: multiple validation datasets
                  def validation_step(self, batch, batch_idx, dataset_idx):
                      # dataset_idx tells you which dataset this is.
  
          .. note:: If you don't need to validate you don't need to implement this method.
  
          .. note:: When the validation_step is called, the model has been put in eval mode
              and PyTorch gradients have been disabled. At the end of validation,
              the model goes back to training mode and gradients are enabled.
          """
  
      def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:
          """
          Use this when validating with dp or ddp2 because validation_step will operate
          on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          .. note:: If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
              validation_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in `validation_step` for each batch part.
  
          Return:
             Dict or OrderedDict - passed to the validation_epoch_end
  
          In this case you should define validation_step_end to perform those calculations.
  
          Examples:
              .. code-block:: python
  
                  # WITHOUT validation_step_end
                  # if used in DP or DDP2, this batch is 1/num_gpus large
                  def validation_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      loss = self.softmax(out)
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
                  # --------------
                  # with validation_step_end to do softmax over the full batch
                  def validation_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      return {'out': out}
  
                  def validation_epoch_end(self, outputs):
                      # this out is now the full size of the batch
                      out = outputs['out']
  
                      # this softmax now uses the full batch size
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
          .. seealso::
              see the :ref:`multi-gpu-training` guide for more details.
          """
  
      def validation_end(self, outputs):
          """
          Warnings:
              Deprecated in v0.7.0. use validation_epoch_end instead. Will be removed 1.0.0
          """
  
      def validation_epoch_end(
              self,
              outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]
      ) -> Dict[str, Dict[str, Tensor]]:
          """
          Called at end of validation epoch with the output of all validation_steps
  
          .. code-block:: python
  
              # the pseudocode for these calls
  
              val_outs = []
              for val_batch in val_data:
                  out = validation_step(train_batch)
                  train_outs.append(out)
              validation_epoch_end(val_outs)
  
          Args:
              outputs: List of outputs you defined in validation_step, or if there are multiple
              dataloaders, a list containing a list of outputs for each dataloader
  
          Return:
              Dict or OrderedDict (dict): Dict has the following optional keys:
              progress_bar -> Dict for progress bar display. Must have only tensors
              log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          .. note:: If you didn't define a validation_step, this won't be called.
  
          - The outputs here are strictly for logging or progress bar.
          - If you don't need to display anything, don't return anything.
          - If you want to manually set current step, you can specify the 'step' key in the 'log' Dict
  
          Examples:
              With a single dataloader
  
              .. code-block:: python
  
                  def validation_epoch_end(self, outputs):
                      val_acc_mean = 0
                      for output in outputs:
                          val_acc_mean += output['val_acc']
  
                      val_acc_mean /= len(outputs)
                      tqdm_dict = {'val_acc': val_acc_mean.item()}
  
                      # show val_loss and val_acc in progress bar but only log val_loss
                      results = {
                          'progress_bar': tqdm_dict,
                          'log': {'val_acc': val_acc_mean.item()}
                      }
                      return results
  
              With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
              one entry per dataloader, while the inner list contains the individual outputs of
              each validation step for that dataloader.
  
              .. code-block:: python
  
                  def validation_epoch_end(self, outputs):
                      val_acc_mean = 0
                      i = 0
                      for dataloader_outputs in outputs:
                          for output in dataloader_outputs:
                              val_acc_mean += output['val_acc']
                              i += 1
  
                      val_acc_mean /= i
                      tqdm_dict = {'val_acc': val_acc_mean.item()}
  
                      # show val_loss and val_acc in progress bar but only log val_loss
                      results = {
                          'progress_bar': tqdm_dict,
                          'log': {'val_acc': val_acc_mean.item(), 'step': self.current_epoch}
                      }
                      return results
          """
  
      def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:
          r"""
          Operate on a single batch of data from the test set
          In this step you'd normally generate examples or calculate anything of interest
          such as accuracy.
  
          .. code-block:: python
  
              # the pseudocode for these calls
  
              test_outs = []
              for test_batch in test_data:
                  out = test_step(train_batch)
                  test_outs.append(out)
              test_epoch_end(test_outs)
  
          Args:
              batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                  dataloader. A tensor, tuple or list
              batch_idx (int): The index of this batch
              dataloader_idx (int): The index of the dataloader that produced this batch
                  (only if multiple test datasets used)
  
          Return:
              Dict or OrderedDict - passed to the test_step_end
  
          .. code-block:: python
  
              # if you have one test dataloader:
              def test_step(self, batch, batch_idx)
  
              # if you have multiple test dataloaders:
              def test_step(self, batch, batch_idx, dataloader_idx)
  
          Examples:
              .. code-block:: python
  
                  # CASE 1: A single test dataset
                  def test_step(self, batch, batch_idx):
                      x, y = batch
  
                      # implement your own
                      out = self(x)
                      loss = self.loss(out, y)
  
                      # log 6 example images
                      # or generated text... or whatever
                      sample_imgs = x[:6]
                      grid = torchvision.utils.make_grid(sample_imgs)
                      self.logger.experiment.add_image('example_images', grid, 0)
  
                      # calculate acc
                      labels_hat = torch.argmax(out, dim=1)
                      val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
  
                      # all optional...
                      # return whatever you need for the collation function validation_end
                      output = OrderedDict({
                          'val_loss': loss_val,
                          'val_acc': torch.tensor(val_acc), # everything must be a tensor
                      })
  
                      # return an optional dict
                      return output
  
              If you pass in multiple validation datasets, validation_step will have an additional
              argument.
  
          .. code-block:: python
  
              # CASE 2: multiple validation datasets
              def test_step(self, batch, batch_idx, dataset_idx):
                  # dataset_idx tells you which dataset this is.
  
          .. note:: If you don't need to validate you don't need to implement this method.
  
          .. note:: When the test_step is called, the model has been put in eval mode and
              PyTorch gradients have been disabled. At the end of the test epoch, the model goes back
              to training mode and gradients are enabled.
          """
  
      def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:
          """
          Use this when testing with dp or ddp2 because test_step will operate
          on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          .. note:: If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
              test_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in `training_step` for each batch part.
  
          Return:
               Dict or OrderedDict - passed to the test_epoch_end
  
          In this case you should define test_step_end to perform those calculations.
  
          Examples:
              .. code-block:: python
  
                  # WITHOUT test_step_end
                  # if used in DP or DDP2, this batch is 1/num_gpus large
                  def test_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      loss = self.softmax(out)
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
                  # --------------
                  # with test_step_end to do softmax over the full batch
                  def test_step(self, batch, batch_idx):
                      # batch is 1/num_gpus big
                      x, y = batch
  
                      out = self(x)
                      return {'out': out}
  
                  def test_step_end(self, outputs):
                      # this out is now the full size of the batch
                      out = outputs['out']
  
                      # this softmax now uses the full batch size
                      loss = nce_loss(loss)
                      return {'loss': loss}
  
          .. seealso::
              see the :ref:`multi-gpu-training` guide for more details.
          """
  
      def test_end(self, outputs):
          """
          Warnings:
               Deprecated in v0.7.0. use test_epoch_end instead. Will be removed 1.0.0
          """
  
      def test_epoch_end(
              self,
              outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]
      ) -> Dict[str, Dict[str, Tensor]]:
          """
          Called at end of test epoch with the output of all test_steps.
  
          .. code-block:: python
  
              # the pseudocode for these calls
  
              test_outs = []
              for test_batch in test_data:
                  out = test_step(test_batch)
                  test_outs.append(out)
              test_epoch_end(test_outs)
  
          Args:
              outputs: List of outputs you defined in test_step, or if there are multiple
              dataloaders, a list containing a list of outputs for each dataloader
  
          Return:
              Dict or OrderedDict (dict): Dict has the following optional keys:
              progress_bar -> Dict for progress bar display. Must have only tensors
              log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          .. note:: If you didn't define a test_step, this won't be called.
  
          - The outputs here are strictly for logging or progress bar.
          - If you don't need to display anything, don't return anything.
          - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict
  
          Examples:
              With a single dataloader
  
              .. code-block:: python
  
                  def test_epoch_end(self, outputs):
                      test_acc_mean = 0
                      for output in outputs:
                          test_acc_mean += output['test_acc']
  
                      test_acc_mean /= len(outputs)
                      tqdm_dict = {'test_acc': test_acc_mean.item()}
  
                      # show test_loss and test_acc in progress bar but only log test_loss
                      results = {
                          'progress_bar': tqdm_dict,
                          'log': {'test_acc': test_acc_mean.item()}
                      }
                      return results
  
              With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
              one entry per dataloader, while the inner list contains the individual outputs of
              each test step for that dataloader.
  
              .. code-block:: python
  
                  def test_epoch_end(self, outputs):
                      test_acc_mean = 0
                      i = 0
                      for dataloader_outputs in outputs:
                          for output in dataloader_outputs:
                              test_acc_mean += output['test_acc']
                              i += 1
  
                      test_acc_mean /= i
                      tqdm_dict = {'test_acc': test_acc_mean.item()}
  
                      # show test_loss and test_acc in progress bar but only log test_loss
                      results = {
                          'progress_bar': tqdm_dict,
                          'log': {'test_acc': test_acc_mean.item(), 'step': self.current_epoch}
                      }
                      return results
          """
  
      def configure_ddp(
              self,
              model: 'LightningModule',
              device_ids: List[int]
      ) -> DistributedDataParallel:
          r"""
          Override to init DDP in your own way or with your own wrapper.
          The only requirements are that:
  
          1. On a validation batch the call goes to model.validation_step.
          2. On a training batch the call goes to model.training_step.
          3. On a testing batch, the call goes to model.test_step
  
          Args:
              model: the LightningModule currently being optimized
              device_ids: the list of GPU ids
  
          Return:
              DDP wrapped model
  
          Examples:
              .. code-block:: python
  
                  # default implementation used in Trainer
                  def configure_ddp(self, model, device_ids):
                      # Lightning DDP simply routes to test_step, val_step, etc...
                      model = LightningDistributedDataParallel(
                          model,
                          device_ids=device_ids,
                          find_unused_parameters=True
                      )
                      return model
  
          """
          model = LightningDistributedDataParallel(
              model,
              device_ids=device_ids,
              find_unused_parameters=True
          )
          return model
  
      def init_ddp_connection(self, proc_rank: int, world_size: int) -> None:
          r"""
  
          Override to define your custom way of setting up a distributed environment.
  
          Lightning's implementation uses env:// init by default and sets the first node as root.
  
          Args:
              proc_rank: The current process rank within the node.
              world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).
  
          Examples:
              .. code-block:: python
  
                  def init_ddp_connection(self):
                      # use slurm job id for the port number
                      # guarantees unique ports across jobs from same grid search
                      try:
                          # use the last 4 numbers in the job id as the id
                          default_port = os.environ['SLURM_JOB_ID']
                          default_port = default_port[-4:]
  
                          # all ports should be in the 10k+ range
                          default_port = int(default_port) + 15000
  
                      except Exception as e:
                          default_port = 12910
  
                      # if user gave a port number, use that one instead
                      try:
                          default_port = os.environ['MASTER_PORT']
                      except Exception:
                          os.environ['MASTER_PORT'] = str(default_port)
  
                      # figure out the root node addr
                      try:
                          root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
                      except Exception:
                          root_node = '127.0.0.2'
  
                      root_node = self.trainer.resolve_root_node_address(root_node)
                      os.environ['MASTER_ADDR'] = root_node
                      dist.init_process_group(
                          'nccl',
                          rank=self.proc_rank,
                          world_size=self.world_size
                      )
  
          """
          # use slurm job id for the port number
          # guarantees unique ports across jobs from same grid search
          try:
              # use the last 4 numbers in the job id as the id
              default_port = os.environ['SLURM_JOB_ID']
              default_port = default_port[-4:]
  
              # all ports should be in the 10k+ range
              default_port = int(default_port) + 15000
  
          except Exception:
              default_port = 12910
  
          # if user gave a port number, use that one instead
          try:
              default_port = os.environ['MASTER_PORT']
          except Exception:
              os.environ['MASTER_PORT'] = str(default_port)
  
          # figure out the root node addr
          try:
              root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
          except Exception:
              root_node = '127.0.0.2'
  
          root_node = self.trainer.resolve_root_node_address(root_node)
          os.environ['MASTER_ADDR'] = root_node
          torch_distrib.init_process_group('nccl', rank=proc_rank, world_size=world_size)
  
      def configure_apex(
              self,
              amp: object,
              model: 'LightningModule',
              optimizers: List[Optimizer],
              amp_level: str
      ) -> Tuple['LightningModule', List[Optimizer]]:
          r"""
          Override to init AMP your own way
          Must return a model and list of optimizers
  
          Args:
              amp: pointer to amp library object
              model: pointer to current lightningModule
              optimizers: list of optimizers passed in configure_optimizers()
              amp_level: AMP mode chosen ('O1', 'O2', etc...)
  
          Return:
              Apex wrapped model and optimizers
  
          Examples:
              .. code-block:: python
  
                  # Default implementation used by Trainer.
                  def configure_apex(self, amp, model, optimizers, amp_level):
                      model, optimizers = amp.initialize(
                          model, optimizers, opt_level=amp_level,
                      )
  
                      return model, optimizers
          """
          model, optimizers = amp.initialize(
              model, optimizers, opt_level=amp_level,
          )
  
          return model, optimizers
  
      def configure_optimizers(self) -> Union[
          Optimizer, List[Optimizer], Tuple[Optimizer, ...], Tuple[List[Optimizer], List]
      ]:
          r"""
          Choose what optimizers and learning-rate schedulers to use in your optimization.
          Normally you'd need one. But in the case of GANs or similar you might have multiple.
  
          If you don't define this method Lightning will automatically use Adam(lr=1e-3)
  
          Return: any of these 5 options:
              - Single optimizer.
              - List or Tuple - List of optimizers.
              - Two lists - The first list has multiple optimizers, the second a list of LR schedulers.
              - Dictionary, with an `optimizer` key and (optionally) a `lr_scheduler` key.
              - Tuple of dictionaries as described, with an optional `frequency` key.
  
          Note:
              The `frequency` value is an int corresponding to the number of sequential batches
              optimized with the specific optimizer. It should be given to none or to all of the optimizers.
              There is difference between passing multiple optimizers in a list,
              and passing multiple optimizers in dictionaries with a frequency of 1:
              In the former case, all optimizers will operate on the given batch in each optimization step.
              In the latter, only one optimizer will operate on the given batch at every step.
  
          Examples:
              .. code-block:: python
  
                  # most cases (default if not defined)
                  def configure_optimizers(self):
                      opt = Adam(self.parameters(), lr=1e-3)
                      return opt
  
                  # multiple optimizer case (eg: GAN)
                  def configure_optimizers(self):
                      generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                      disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                      return generator_opt, disriminator_opt
  
                  # example with learning_rate schedulers
                  def configure_optimizers(self):
                      generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                      disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                      discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
                      return [generator_opt, disriminator_opt], [discriminator_sched]
  
                  # example with step-based learning_rate schedulers
                  def configure_optimizers(self):
                      gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                      dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                      gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),
                                   'interval': 'step'}  # called after each training step
                      dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch
                      return [gen_opt, dis_opt], [gen_sched, dis_sched]
  
                  # example with optimizer frequencies
                  # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
                  # https://arxiv.org/abs/1704.00028
                  def configure_optimizers(self):
                      gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                      dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                      n_critic = 5
                      return (
                          {'optimizer': dis_opt, 'frequency': n_critic},
                          {'optimizer': gen_opt, 'frequency': 1}
                      )
  
          Note:
  
              Some things to know:
  
              - Lightning calls ``.backward()`` and ``.step()`` on each optimizer
                and learning rate scheduler as needed.
  
              - If you use 16-bit precision (``precision=16``), Lightning will automatically
                handle the optimizers for you.
  
              - If you use multiple optimizers, training_step will have an additional
                ``optimizer_idx`` parameter.
  
              - If you use LBFGS lightning handles the closure function automatically for you
  
              - If you use multiple optimizers, gradients will be calculated only
                for the parameters of current optimizer at each training step.
  
              - If you need to control how often those optimizers step or override the
                default .step() schedule, override the `optimizer_step` hook.
  
              - If you only want to call a learning rate scheduler every `x` step or epoch,
                or want to monitor a custom metric, you can specify these in a dictionary:
  
                .. code-block:: python
  
                    {
                        'scheduler': lr_scheduler,
                        'interval': 'step'  # or 'epoch'
                        'monitor': 'val_f1',
                        'frequency': x
                    }
  
          """
          return Adam(self.parameters(), lr=1e-3)
  
      def optimizer_step(
              self,
              epoch: int,
              batch_idx: int,
              optimizer: Optimizer,
              optimizer_idx: int,
              second_order_closure: Optional[Callable] = None,
      ) -> None:
          r"""
  
          Override this method to adjust the default way the Trainer calls each optimizer.
          By default, Lightning calls .step() and zero_grad() as shown in the example
          once per optimizer.
  
          Args:
              epoch: Current epoch
              batch_idx: Index of current batch
              optimizer: A PyTorch optimizer
              optimizer_idx: If you used multiple optimizers this indexes into that list
              second_order_closure: closure for second order methods
  
          Examples:
              .. code-block:: python
  
                  # DEFAULT
                  def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                     second_order_closure=None):
                      optimizer.step()
                      optimizer.zero_grad()
  
                  # Alternating schedule for optimizer steps (ie: GANs)
                  def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                     second_order_closure=None):
                      # update generator opt every 2 steps
                      if optimizer_idx == 0:
                          if batch_idx % 2 == 0 :
                              optimizer.step()
                              optimizer.zero_grad()
  
                      # update discriminator opt every 4 steps
                      if optimizer_idx == 1:
                          if batch_idx % 4 == 0 :
                              optimizer.step()
                              optimizer.zero_grad()
  
                      # ...
                      # add as many optimizers as you want
  
  
              Here's another example showing how to use this for more advanced things such as
              learning-rate warm-up:
  
              .. code-block:: python
  
                  # learning rate warm-up
                  def optimizer_step(self, current_epoch, batch_idx, optimizer,
                                      optimizer_idx, second_order_closure=None):
                      # warm up lr
                      if self.trainer.global_step < 500:
                          lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
                          for pg in optimizer.param_groups:
                              pg['lr'] = lr_scale * self.hparams.learning_rate
  
                      # update params
                      optimizer.step()
                      optimizer.zero_grad()
  
          """
          if self.trainer.use_tpu and XLA_AVAILABLE:
              xm.optimizer_step(optimizer)
          elif isinstance(optimizer, torch.optim.LBFGS):
              optimizer.step(second_order_closure)
          else:
              optimizer.step()
  
          # clear gradients
          optimizer.zero_grad()
  
      def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:
          r"""
  
          When using truncated backpropagation through time, each batch must be split along the
          time dimension. Lightning handles this by default, but  for custom behavior override
          this function.
  
          Args:
              batch: Current batch
              split_size: How big the split  is
  
          Return:
              list of batch splits. Each split will be passed to forward_step to enable truncated
              back propagation through time. The default implementation splits root level Tensors and
              Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
  
          Examples:
              .. code-block:: python
  
                  def tbptt_split_batch(self, batch, split_size):
                    splits = []
                    for t in range(0, time_dims[0], split_size):
                        batch_split = []
                        for i, x in enumerate(batch):
                            if isinstance(x, torch.Tensor):
                                split_x = x[:, t:t + split_size]
                            elif isinstance(x, collections.Sequence):
                                split_x = [None] * len(x)
                                for batch_idx in range(len(x)):
                                    split_x[batch_idx] = x[batch_idx][t:t + split_size]
  
                            batch_split.append(split_x)
  
                        splits.append(batch_split)
  
                    return splits
  
          .. note:: Called in the training loop after on_batch_start if ``truncated_bptt_steps > 0``.
              Each returned batch split is passed separately to ``training_step(...)``.
  
          """
          time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))]
          assert len(time_dims) >= 1, "Unable to determine batch time dimension"
          assert all(x == time_dims[0] for x in time_dims), "Batch time dimension length is ambiguous"
  
          splits = []
          for t in range(0, time_dims[0], split_size):
              batch_split = []
              for i, x in enumerate(batch):
                  if isinstance(x, torch.Tensor):
                      split_x = x[:, t:t + split_size]
                  elif isinstance(x, collections.Sequence):
                      split_x = [None] * len(x)
                      for batch_idx in range(len(x)):
                          split_x[batch_idx] = x[batch_idx][t:t + split_size]
  
                  batch_split.append(split_x)
  
              splits.append(batch_split)
  
          return splits
  
      def prepare_data(self) -> None:
          """Use this to download and prepare data.
          In distributed (GPU, TPU), this will only be called once
  
          Return:
              PyTorch DataLoader
  
          This is called before requesting the dataloaders
  
          .. code-block:: python
  
              model.prepare_data()
              model.train_dataloader()
              model.val_dataloader()
              model.test_dataloader()
  
          Examples:
              .. code-block:: python
  
                  def prepare_data(self):
                      download_imagenet()
                      clean_imagenet()
                      cache_imagenet()
          """
  
      def train_dataloader(self) -> DataLoader:
          """Implement a PyTorch DataLoader
  
          Return:
              PyTorch DataLoader
  
          Return a dataloader. It will not be called every epoch unless you set
          ```Trainer(reload_dataloaders_every_epoch=True)```.
  
          It's recommended that all data downloads and preparation happen in prepare_data().
  
          .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.
              No need to set yourself.
  
              - .fit()
              - ...
              - prepare_data()
              - train_dataloader
  
          Example:
              .. code-block:: python
  
                  def train_dataloader(self):
                      transform = transforms.Compose([transforms.ToTensor(),
                                                      transforms.Normalize((0.5,), (1.0,))])
                      dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
                                      download=True)
                      loader = torch.utils.data.DataLoader(
                          dataset=dataset,
                          batch_size=self.hparams.batch_size,
                          shuffle=True
                      )
                      return loader
  
          """
  
      def tng_dataloader(self):  # todo: remove in v1.0.0
          """Implement a PyTorch DataLoader.
  
          Warnings:
              Deprecated in v0.5.0. use train_dataloader instead. Will be removed 1.0.0
          """
          output = self.train_dataloader()
          warnings.warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0."
                        " and this method will be removed in v1.0.0", DeprecationWarning)
          return output
  
      def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
          r"""
  
          Return a dataloader. It will not be called every epoch unless you set
          ```Trainer(reload_dataloaders_every_epoch=True)```.
  
          It's recommended that all data downloads and preparation happen in prepare_data().
  
              - .fit()
              - ...
              - prepare_data()
              - train_dataloader
              - val_dataloader
              - test_dataloader
  
          .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.
              No need to set yourself.
  
          Return:
              Single or multiple PyTorch DataLoader
  
          Example:
              .. code-block:: python
  
                  def test_dataloader(self):
                      transform = transforms.Compose([transforms.ToTensor(),
                                                      transforms.Normalize((0.5,), (1.0,))])
                      dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                                      download=True)
                      loader = torch.utils.data.DataLoader(
                          dataset=dataset,
                          batch_size=self.hparams.batch_size,
                          shuffle=True
                      )
  
                      return loader
  
          .. note:: If you don't need a test dataset and a test_step, you don't need to implement
              this method.
  
          """
  
      def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
          r"""
  
          Return a dataloader. It will not be called every epoch unless you set
          ```Trainer(reload_dataloaders_every_epoch=True)```.
  
          It's recommended that all data downloads and preparation happen in prepare_data().
  
              - .fit()
              - ...
              - prepare_data()
              - train_dataloader
              - val_dataloader
  
          .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware
              No need to set yourself.
  
          Return:
              Single or multiple PyTorch DataLoader
  
          Examples:
              .. code-block:: python
  
                  def val_dataloader(self):
                      transform = transforms.Compose([transforms.ToTensor(),
                                                      transforms.Normalize((0.5,), (1.0,))])
                      dataset = MNIST(root='/path/to/mnist/', train=False,
                                      transform=transform, download=True)
                      loader = torch.utils.data.DataLoader(
                          dataset=dataset,
                          batch_size=self.hparams.batch_size,
                          shuffle=True
                      )
  
                      return loader
  
                  # can also return multiple dataloaders
                  def val_dataloader(self):
                      return [loader_a, loader_b, ..., loader_n]
  
              .. code-block:: python
  
                  def val_dataloader(self):
                      transform = transforms.Compose([transforms.ToTensor(),
                                  transforms.Normalize((0.5,), (1.0,))])
                      dataset = MNIST(root='/path/to/mnist/', train=False,
                                transform=transform, download=True)
                      loader = torch.utils.data.DataLoader(
                          dataset=dataset,
                          batch_size=self.hparams.batch_size,
                          shuffle=True
                      )
  
                      return loader
  
                  # can also return multiple dataloaders
                  def val_dataloader(self):
                      return [loader_a, loader_b, ..., loader_n]
  
          .. note:: If you don't need a validation dataset and a validation_step, you don't need to
              implement this method.
  
          .. note:: In the case where you return multiple `val_dataloaders`, the `validation_step`
              will have an argument `dataset_idx` which matches the order here.
          """
  
      @classmethod
      def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
          r"""
          Warning:
              Deprecated in version 0.7.0. You should use `load_from_checkpoint` instead.
               Will be removed in v0.9.0.
          """
          warnings.warn(
              "`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0."
              " The deprecated method will be removed in v0.9.0.", DeprecationWarning
          )
          return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)
  
      @classmethod
      def load_from_checkpoint(
              cls,
              checkpoint_path: str,
              map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
              tags_csv: Optional[str] = None,
              *args, **kwargs
      ) -> 'LightningModule':
          r"""
  
          Primary way of loading model from a checkpoint. When Lightning saves a checkpoint
          it stores the hyperparameters in the checkpoint if you initialized your LightningModule
          with an argument called `hparams` which is a Namespace (output of using argparse
          to parse command line arguments).
  
          Example:
              .. code-block:: python
  
                  from argparse import Namespace
                  hparams = Namespace(**{'learning_rate': 0.1})
  
                  model = MyModel(hparams)
  
                  class MyModel(LightningModule):
                      def __init__(self, hparams):
                          self.learning_rate = hparams.learning_rate
  
          Args:
              checkpoint_path: Path to checkpoint.
              model_args: Any keyword args needed to init the model.
              map_location:
                  If your checkpoint saved a GPU model and you now load on CPUs
                  or a different number of GPUs, use this to map to the new setup.
                  The behaviour is the same as in
                  `torch.load <https://pytorch.org/docs/stable/torch.html#torch.load>`_.
              tags_csv: Optional path to a .csv file with two columns (key, value)
                  as in this example::
  
                      key,value
                      drop_prob,0.2
                      batch_size,32
  
                  You most likely won't need this since Lightning will always save the hyperparameters
                  to the checkpoint.
                  However, if your checkpoint weights don't have the hyperparameters saved,
                  use this method to pass in a .csv file with the hparams you'd like to use.
                  These will be converted into a argparse.Namespace and passed into your
                  LightningModule for use.
  
          Return:
              LightningModule with loaded weights and hyperparameters (if available).
  
          Example:
              .. code-block:: python
  
                  # load weights without mapping ...
                  MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
  
                  # or load weights mapping all weights from GPU 1 to GPU 0 ...
                  map_location = {'cuda:1':'cuda:0'}
                  MyLightningModule.load_from_checkpoint(
                      'path/to/checkpoint.ckpt',
                      map_location=map_location
                  )
  
                  # or load weights and hyperparameters from separate files.
                  MyLightningModule.load_from_checkpoint(
                      'path/to/checkpoint.ckpt',
                      tags_csv='/path/to/hparams_file.csv'
                  )
  
                  # or load passing whatever args the model takes to load
                  MyLightningModule.load_from_checkpoint(
                      'path/to/checkpoint.ckpt',
                      learning_rate=0.1,
                      layers=2,
                      pretrained_model=some_model
                  )
  
                  # predict
                  pretrained_model.eval()
                  pretrained_model.freeze()
                  y_hat = pretrained_model(x)
          """
          if map_location is not None:
              checkpoint = torch.load(checkpoint_path, map_location=map_location)
          else:
              checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
  
          if tags_csv is not None:
              # add the hparams from csv file to checkpoint
              hparams = load_hparams_from_tags_csv(tags_csv)
              hparams.__setattr__('on_gpu', False)
              checkpoint['hparams'] = vars(hparams)
  
          model = cls._load_model_state(checkpoint, *args, **kwargs)
          return model
  
      @classmethod
      def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':
          cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters
          ckpt_hparams = checkpoint.get('hparams')
  
          if cls_takes_hparams:
              if ckpt_hparams is not None:
                  is_namespace = checkpoint.get('hparams_type', 'namespace') == 'namespace'
                  hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams
              else:
                  warnings.warn(
                      f"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ "
                      f"contains argument 'hparams'. Will pass in an empty Namespace instead."
                      " Did you forget to store your model hyperparameters in self.hparams?"
                  )
                  hparams = Namespace()
          else:  # The user's LightningModule does not define a hparams argument
              if ckpt_hparams is None:
                  hparams = None
              else:
                  raise MisconfigurationException(
                      f"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ "
                      f"is missing the argument 'hparams'. Are you loading the correct checkpoint?"
                  )
  
          # load the state_dict on the model automatically
          model_args = [hparams] if hparams else []
          if len(model_args) > 0:
              model = cls(*model_args)
          else:
              model = cls(*args, **kwargs)
          model.load_state_dict(checkpoint['state_dict'])
  
          # give model a chance to load something
          model.on_load_checkpoint(checkpoint)
  
          return model
  
      def summarize(self, mode: str) -> None:
          model_summary = ModelSummary(self, mode=mode)
          log.info('\n' + model_summary.__str__())
  
      def freeze(self) -> None:
          r"""
          Freeze all params for inference
  
          Example:
              .. code-block:: python
  
                  model = MyLightningModule(...)
                  model.freeze()
  
          """
          for param in self.parameters():
              param.requires_grad = False
  
          self.eval()
  
      def unfreeze(self) -> None:
          """Unfreeze all params for training.
  
          .. code-block:: python
  
              model = MyLightningModule(...)
              model.unfreeze()
  
          """
          for param in self.parameters():
              param.requires_grad = True
  
          self.train()
  
      def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          r"""
          Called by lightning to restore your model.
          If you saved something with **on_save_checkpoint** this is your chance to restore this.
  
          Args:
              checkpoint: Loaded checkpoint
  
  
          Example:
              .. code-block:: python
  
                  def on_load_checkpoint(self, checkpoint):
                      # 99% of the time you don't need to implement this method
                      self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
  
          .. note:: Lighting auto-restores global step, epoch, and train state including amp scaling.
              No need for you to restore anything regarding training.
          """
  
      def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          r"""
  
          Called by lightning when saving a  checkpoint  to give you a chance to store anything
          else you might want to  save
  
          Args:
              checkpoint: Checkpoint to be saved
  
          Example:
              .. code-block:: python
  
                  def on_save_checkpoint(self, checkpoint):
                      # 99% of use cases you don't need to implement this method
                      checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
  
          .. note:: Lighting saves all aspects of training (epoch, global step, etc...)
              including amp scaling. No need
              for you to store anything about training.
  
          """
  
      def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:
          r"""
          Additional items to be displayed in the progress bar.
  
          Return:
              Dictionary with the items to be displayed in the progress bar.
          """
          # call .item() only once but store elements without graphs
          running_train_loss = self.trainer.running_loss.mean()
          avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')
          tqdm_dict = {
              'loss': '{:.3f}'.format(avg_training_loss)
          }
  
          if self.trainer.truncated_bptt_steps is not None:
              tqdm_dict['split_idx'] = self.trainer.split_idx
  
          if self.trainer.logger is not None and self.trainer.logger.version is not None:
              tqdm_dict['v_num'] = self.trainer.logger.version
  
          return tqdm_dict
  